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--- |
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datasets: |
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- sciq |
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- metaeval/ScienceQA_text_only |
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- GAIR/lima |
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- Open-Orca/OpenOrca |
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- openbookqa |
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language: |
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- en |
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tags: |
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- upstage |
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- llama |
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- instruct |
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- instruction |
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pipeline_tag: text-generation |
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--- |
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# LLaMa-2-70b-instruct-1024 model card |
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## Model Details |
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* **Developed by**: [Upstage](https://en.upstage.ai) |
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* **Backbone Model**: [LLaMA-2](https://github.com/facebookresearch/llama/tree/main) |
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* **Language(s)**: English |
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* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers) |
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* **License**: Fine-tuned checkpoints is licensed under the Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/)) |
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* **Where to send comments**: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the [Hugging Face community's model repository](https://huggingface.co/upstage/Llama-2-70b-instruct-1024/discussions) |
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* **Contact**: For questions and comments about the model, please email `[email protected]` |
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## Dataset Details |
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### Used Datasets |
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- [openbookqa](https://huggingface.co/datasets/openbookqa) |
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- [sciq](https://huggingface.co/datasets/sciq) |
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- [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) |
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- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) |
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- [GAIR/lima](https://huggingface.co/datasets/GAIR/lima) |
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> No other data was used except for the dataset mentioned above |
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### Prompt Template |
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``` |
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### System: |
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{System} |
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### User: |
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{User} |
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### Assistant: |
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{Assistant} |
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``` |
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## Hardware and Software |
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* **Hardware**: We utilized an A100x8 for training our model |
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* **Training Factors**: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace trainer](https://huggingface.co/docs/transformers/main_classes/trainer) |
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## Evaluation Results |
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### Overview |
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- We conducted a performance evaluation based on the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). |
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We evaluated our model on four benchmark datasets, which include `ARC-Challenge`, `HellaSwag`, `MMLU`, and `TruthfulQA`. |
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We used the [lm-evaluation-harness repository](https://github.com/EleutherAI/lm-evaluation-harness), specifically commit [b281b0921b636bc36ad05c0b0b0763bd6dd43463](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463). |
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### Main Results |
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| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | |
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|-----------------------------------------------|---------|-------|-----------|-------|------------| |
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| Llama-2-70b-instruct-1024 (***Ours***, ***Local Reproduction***) | **72.02** | **70.73** | **87.41** | **69.27** | **60.68** | |
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| llama-65b-instruct (***Ours***, ***Local Reproduction***) | 69.4 | 67.6 | 86.5 | 64.9 | 58.8 | |
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| llama-30b-instruct-2048 (***Ours***, ***Open LLM Leaderboard***) | 67.0 | 64.9 | 84.9 | 61.9 | 56.3 | |
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| Llama-2-70b-chat-hf | 66.8 | 64.6 | 85.9 | 63.9 | 52.8 | |
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| llama-30b-instruct (***Ours***, ***Open LLM Leaderboard***) | 65.2 | 62.5 | 86.2 | 59.4 | 52.8 | |
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| falcon-40b-instruct | 63.4 | 61.6 | 84.3 | 55.4 | 52.5 | |
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| llama-65b | 62.1 | 57.6 | 84.3 | 63.4 | 43.0 | |
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### Scripts |
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- Prepare evaluation environments: |
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``` |
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# clone the repository |
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git clone https://github.com/EleutherAI/lm-evaluation-harness.git |
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# check out the specific commit |
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git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463 |
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# change to the repository directory |
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cd lm-evaluation-harness |
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``` |
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## Ethical Issues |
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### Ethical Considerations |
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- There were no ethical issues involved, as we did not include the benchmark test set or the training set in the model's training process. |
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## Contact Us |
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### Why Upstage LLM? |
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- [Upstage](https://en.upstage.ai)'s LLM research has yielded remarkable results. Our 30B model **outperforms all models around the world**, positioning itself as the leading performer. Recognizing the immense potential in implementing private LLM to actual businesses, we invite you to easily apply private LLM and fine-tune it with your own data. For a seamless and tailored solution, please do not hesitate to reach out to us. ► [click here to contact]. |
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[click here to contact]: mailto:[email protected] |
